{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "provenance": [],
   "machine_shape": "hm",
   "gpuType": "T4"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  },
  "accelerator": "GPU"
 },
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 数据集导入(预制链接)"
   ],
   "metadata": {
    "id": "kBRw5QHhBkax"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 官方版本数据导入"
   ],
   "metadata": {
    "id": "gaD7ugivEL2R"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hfHjWRgKBR47",
    "outputId": "6076e88e-b8f7-4882-b32a-98b861aa1c82"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--2024-07-12 09:10:28--  https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=dc3aa13c-c3a9-458f-983a-8586798cb635&at=APZUnTX25XMxi-z-3wBcgR93IGsL%3A1719235792953\n",
      "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 64.233.170.132, 2404:6800:4003:c1a::84\n",
      "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|64.233.170.132|:443... connected.\n",
      "HTTP request sent, awaiting response... 416 Requested range not satisfiable\n",
      "\n",
      "    The file is already fully retrieved; nothing to do.\n",
      "\n",
      "Archive:  Dataset.zip\n",
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      "  inflating: Dataset/Training_data/Label/press_657.npy  \n",
      "  inflating: Dataset/Training_data/train_pressure_min_std.txt  \n",
      "  inflating: Dataset/Training_data/watertight_global_bounds.txt  \n",
      "  inflating: Dataset/Training_data/watertight_meshes.txt  \n"
     ]
    }
   ],
   "source": [
    "!wget --header=\"Host: drive.usercontent.google.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Cookie: __Secure-ENID=12.SE=Yd0Bj-CLJ14fnd4qzdJHmwUs4B5zz46UaPC1cPJigNqqFV9PtM2CYyBpSbCkOyzUwzlEdZ1nZFf-igtGi7wSdJ_gqQSfQfh84r9egqFQAy9-GKayCRbdQKdera-2mkpuIT-c64CyR9vfNojM3hxZ9Dej-dGvtxlGjal9ttEHybw; __gsas=ID=ae0421b9a34b478c:T=1710758437:RT=1710758437:S=ALNI_MZP13R9ZOHbCzC0rgHSMrGXj6GCsg; HSID=A-4I-ZudDNUIB6EKH; SSID=A7v_1v9un6xAwVNku; APISID=ctK8IbLjeuDUmgys/AFnMSLWt9KddceDI6; SAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; __Secure-1PAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; __Secure-3PAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; SID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_kzuBV1TvOhAIC8VF1e9fpgACgYKATQSARQSFQHGX2Mi8LXUwWoIwNCEPU8Sy3mXUxoVAUF8yKqGXVfjTGz9gQal7nwGr4Pl0076; __Secure-1PSID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_PDa-DzVmbdGFPyxMQpk9_QACgYKAewSARQSFQHGX2MiAeee4fn0OWglWZfAygqkyBoVAUF8yKp-Sfmtnueimxc-0QbJRF9I0076; __Secure-3PSID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_g9IrMeU98APBo9Stp6wEnAACgYKAQASARQSFQHGX2MiFWtc9ucONXnpxBzlRdudEhoVAUF8yKoeZwCpJDnjfAFjGssHSUGm0076; NID=515=GQhY9nKKFCx3qFDjE0MA4ubjWNdef6xCIY_RfWOPWKEtyfBN3nAUl8WHI2VczjNQ4rVkj1XBAY8WNWHXyqSK10CfT4FxsFlPzrHIJpeTtm1nWRNBd9AAfBKJHz4XpESszntVUTE_59RklZuKo0vk1poReVi2da1PZKC3CTKH2Ll3gB5xuB9wf4bmq8ylVUuIROPJczr0XnCuUHV3qLdBvgy9_870b6UwOq1iOlIxFQFm01EZ4pqF4q1Ub3QRSWpEMLh4LSZFpJ5O255R5OV7krmEdDvH_sHoTEPZAg2PoEpwAyGK6Xp9qcLIlldgx5-5V86N8Wtb93uTlQuA_CFXb5_2eP3bgeX8txwlJ5SrldVjg9ctzYtBU2RwJKTSvdHfIG7lpOkg6XlkvDOcJpR3DihT_OlqnPn7drCAJpvVDv29hZn5XPMXaSrNdbG64OJ9urJEw5odEwsLYkkpC1vmlUcuoo52S5f6RQu0Z8kZiV8iRW6XIqHsSmQHunVaxk6xWCStUg; __Secure-1PSIDTS=sidts-CjEB3EgAEtTS0OazynCofIH4RCBstiRP5flEcvYW3z4Fg9oGd5QOESDOZt1wO2iqUYHjEAA; __Secure-3PSIDTS=sidts-CjEB3EgAEtTS0OazynCofIH4RCBstiRP5flEcvYW3z4Fg9oGd5QOESDOZt1wO2iqUYHjEAA; SIDCC=AKEyXzVI6aMX8lSDja86Yts3FBAtBzPCzVNgaX5BCz78NWsWzlT3yFWKUV7ZE46SFzE1GiBI-cHdTw; __Secure-1PSIDCC=AKEyXzUo4NQAwqqPMxP2eye-MFEbZmBIm_sZqRU1amttg0YoQkc8ZKSNXdHl5jNCMEbhrUHhS9-K; __Secure-3PSIDCC=AKEyXzWf2lIdmDLeZKpXSi9GytVQb6XudrYiNUBA5gW952YuLh8kL6T3IbBlu8zOTfGEcdUp5O1R\" --header=\"Connection: keep-alive\" \"https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=dc3aa13c-c3a9-458f-983a-8586798cb635&at=APZUnTX25XMxi-z-3wBcgR93IGsL%3A1719235792953\" -c -O 'Dataset.zip'\n",
    "!unzip Dataset.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "id": "ITzT8s2wgZG0"
   }
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "!wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/38e9adf0fce84527aad3558cc3e82d0e9a251aac4c934297afae9b74d9b3d1e9?responseContentDisposition=attachment%3B%20filename%3Dtrain_track_B.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-04T03%3A21%3A02Z%2F-1%2F%2Facd359add161bace603a52c7a268467406cb3c1889a7114bbb687de8002b55f6\" -c -O 'train_track_B.zip'\n",
    "!wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/7877c2fd300345599ed3365feda50425f4857caa71bf4af9bf047fb08e35aa97?responseContentDisposition=attachment%3B%20filename%3Dmesh_B_0603.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-04T11%3A48%3A26Z%2F-1%2F%2Fec6cce492ba6afff841ef197860065742a7ba8220def02c1cbc311333aa993b2\" -c -O 'mesh_B_0603.zip'"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "!mkdir -p train_track_B && unzip -o train_track_B.zip -d train_track_B/\n",
    "!mkdir -p mesh_B_0603 && unzip -o mesh_B_0603.zip -d mesh_B_0603/"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "!mkdir Dataset/data_train_B/\n",
    "!mkdir Dataset/data_valid_B/"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "l1=os.listdir(\"train_track_B/\")\n",
    "l1=sorted([i for i in l1 if 'centroid_' in i])\n",
    "train_df=pd.DataFrame(l1,columns=['id'])\n",
    "train_df['id']=train_df['id'].apply(lambda x:x[9:13])\n",
    "train_df['id']=train_df['id'].astype(int)\n",
    "valid_df=train_df[train_df.index>=450].reset_index(drop=True)\n",
    "train_df=train_df[train_df.index<450].reset_index(drop=True)\n",
    "train_df.to_csv('Dataset/data_train_B/train_index.txt',index=False,header=None)\n",
    "valid_df.to_csv('Dataset/data_valid_B/train_index.txt',index=False,header=None)"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "for file_id in train_df['id']:\n",
    "  os.system(f'mv train_track_B/centroid_{str(file_id).zfill(4)}.npy Dataset/data_train_B/')\n",
    "  os.system(f'mv mesh_B_0603/train_mesh_0603/mesh_{str(file_id).zfill(4)}.ply Dataset/data_train_B/')\n",
    "  os.system(f'mv train_track_B/press_{str(file_id).zfill(4)}.npy Dataset/data_train_B/')"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "for file_id in valid_df['id']:\n",
    "  os.system(f'mv train_track_B/centroid_{str(file_id).zfill(4)}.npy Dataset/data_valid_B/')\n",
    "  os.system(f'mv mesh_B_0603/train_mesh_0603/mesh_{str(file_id).zfill(4)}.ply Dataset/data_valid_B/')\n",
    "  os.system(f'mv train_track_B/press_{str(file_id).zfill(4)}.npy Dataset/data_valid_B/')"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# !rm -rf Dataset"
   ],
   "metadata": {
    "id": "IR-oWQomArX8"
   },
   "execution_count": 48,
   "outputs": []
  },
  {
   "metadata": {
    "id": "SkTWdadl8tXh"
   },
   "cell_type": "code",
   "outputs": [],
   "execution_count": 50,
   "source": [
    "!mkdir Dataset/data_test_B/\n",
    "!mv Dataset/Testset_track_B/Inference/* Dataset/data_test_B/\n",
    "!mv Dataset/Testset_track_B/Auxiliary/* Dataset/data_test_B/"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import os\n",
    "l1=os.listdir('Dataset/data_test_B/')\n",
    "for file in l1:\n",
    "  if 'centroid_' in file or 'mesh_' in file:\n",
    "    file_id=file.split('_')[1].split('.')[0]\n",
    "    new_file=file.split('_')[0]+'_'+file_id.zfill(3)+'.'+file.split('.')[1]\n",
    "    os.system(f'mv Dataset/data_test_B/{file} Dataset/data_test_B/{new_file}')"
   ],
   "metadata": {
    "id": "nEmc5uvUEdji"
   },
   "execution_count": 51,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "l1=os.listdir(\"Dataset/data_test_B/\")\n",
    "l1=sorted([i for i in l1 if 'centroid_' in i])\n",
    "test_df=pd.DataFrame(l1,columns=['id'])\n",
    "test_df['id']=test_df['id'].apply(lambda x:x[9:12])\n",
    "test_df['id']=test_df['id'].astype(int)\n",
    "test_df.to_csv('Dataset/data_test_B/train_index.txt',index=False,header=None)"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# import pandas as pd\n",
    "# pd.DataFrame([1]).to_csv('Dataset/data_test_B/train_index.txt',index=False,header=None)\n",
    "# !mkdir Dataset/data_train_B/\n",
    "# !cp *.txt Dataset/data_train_B/\n",
    "# !cp mesh_0002.ply Dataset/data_train_B/\n",
    "# !cp press_0002.npy Dataset/data_train_B/"
   ],
   "metadata": {
    "id": "Dzv1EoeYDcL3"
   },
   "execution_count": 52,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install paddlepaddle-gpu==2.6.1.post120 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html\n",
    "!pip install open3d -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
    "!pip install plyfile -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
    "!pip install loguru"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XLNZMxywPYe1",
    "outputId": "40244b88-629a-44ce-cfe5-7c087091584e"
   },
   "execution_count": 53,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Looking in links: https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html\n",
      "Requirement already satisfied: paddlepaddle-gpu==2.6.1.post120 in /usr/local/lib/python3.10/dist-packages (2.6.1.post120)\n",
      "Requirement already satisfied: httpx in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.6.1.post120) (0.27.0)\n",
      "Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.6.1.post120) (1.25.2)\n",
      "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.6.1.post120) (9.4.0)\n",
      "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.6.1.post120) (4.4.2)\n",
      "Requirement already satisfied: astor in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.6.1.post120) (0.8.1)\n",
      "Requirement already satisfied: opt-einsum==3.3.0 in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.6.1.post120) (3.3.0)\n",
      "Requirement already satisfied: protobuf>=3.20.2 in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.6.1.post120) (3.20.3)\n",
      "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx->paddlepaddle-gpu==2.6.1.post120) (3.7.1)\n",
      "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx->paddlepaddle-gpu==2.6.1.post120) (2024.6.2)\n",
      "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx->paddlepaddle-gpu==2.6.1.post120) (1.0.5)\n",
      "Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx->paddlepaddle-gpu==2.6.1.post120) (3.7)\n",
      "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx->paddlepaddle-gpu==2.6.1.post120) (1.3.1)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx->paddlepaddle-gpu==2.6.1.post120) (0.14.0)\n",
      "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx->paddlepaddle-gpu==2.6.1.post120) (1.2.1)\n",
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: open3d in /usr/local/lib/python3.10/dist-packages (0.18.0)\n",
      "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.10/dist-packages (from open3d) (1.25.2)\n",
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      "Requirement already satisfied: addict in /usr/local/lib/python3.10/dist-packages (from open3d) (2.4.0)\n",
      "Requirement already satisfied: pillow>=9.3.0 in /usr/local/lib/python3.10/dist-packages (from open3d) (9.4.0)\n",
      "Requirement already satisfied: matplotlib>=3 in /usr/local/lib/python3.10/dist-packages (from open3d) (3.7.1)\n",
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      "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly>=5.0.0->dash>=2.6.0->open3d) (8.4.2)\n",
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      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->dash>=2.6.0->open3d) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->dash>=2.6.0->open3d) (2.0.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->dash>=2.6.0->open3d) (2024.6.2)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets>=8.0.4->open3d) (0.8.4)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets>=8.0.4->open3d) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=6.1.0->ipywidgets>=8.0.4->open3d) (0.2.13)\n",
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: plyfile in /usr/local/lib/python3.10/dist-packages (1.0.3)\n",
      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from plyfile) (1.25.2)\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import paddle\n",
    "paddle.utils.run_check()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PK_IRye3DV0u",
    "outputId": "3d79a491-9e97-4a62-9bba-cbd1d6dc2b5d"
   },
   "execution_count": 54,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Running verify PaddlePaddle program ... \n",
      "PaddlePaddle works well on 1 GPU.\n",
      "PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 额外数据导入\n",
    "（此处导入权重文件和额外数据集，在此之外的导入将有被判违规的风险，这里以导入随机生成的Track C的A榜样例提交的zip为例子）"
   ],
   "metadata": {
    "id": "DUxmPjWWV1sr"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 主要库版本检查以及随机种子锁定"
   ],
   "metadata": {
    "id": "p7YDBByYeYsB"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 随机种子锁定"
   ],
   "metadata": {
    "id": "90Bu7fXRftyr"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 以C榜为例的输出规范\n",
    "(将答案文件夹压缩为B_result.zip,用于被程序识别，假设文件路径是Example_C)"
   ],
   "metadata": {
    "id": "MBCalHN7bBji"
   }
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "!cp *.txt Dataset/data_train_B/\n",
    "!cp *.txt Dataset/Testset_track_B/Inference/\n",
    "!cp *.txt Dataset/data_valid_B/"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "if os.path.exists('model-UNet.pdparams'):\n",
    "  os.system('python paddle_unet_infer.py')\n",
    "else:\n",
    "  os.system('python paddle_unet_train.py')"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "!mkdir -p train_track_B && unzip -o train_track_B.zip -d train_track_B/"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "if os.path.exists('best_valid_checkpoin_L3090-B054.pt'):\n",
    "  os.system('python torch_infer.py')\n",
    "else:\n",
    "  os.system('python torch_train.py')"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "import zipfile\n",
    "import numpy as np\n",
    "pred_01={}\n",
    "# 打开ZIP文件\n",
    "with zipfile.ZipFile('B_result_unet.zip', 'r') as zip_file:\n",
    "  # 遍历ZIP文件中的所有文件和文件夹\n",
    "  for file_info in zip_file.infolist():\n",
    "    # 获取子文件名\n",
    "    file_name = file_info.filename\n",
    "\n",
    "    # 如果是文件（而不是文件夹），则读取内容\n",
    "    if not file_info.is_dir() and file_name.endswith('.npy') and 'press_0' not in file_name:\n",
    "      # 读取子文件内容\n",
    "      with zip_file.open(file_name, 'r') as sub_file:\n",
    "        data = np.load(sub_file)\n",
    "        pred_01[file_name]=data\n",
    "        print(file_name)"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "import zipfile\n",
    "\n",
    "pred_02={}\n",
    "# 打开ZIP文件\n",
    "with zipfile.ZipFile('B_result_torch.zip', 'r') as zip_file:\n",
    "  # 遍历ZIP文件中的所有文件和文件夹\n",
    "  for file_info in zip_file.infolist():\n",
    "    # 获取子文件名\n",
    "    file_name = file_info.filename\n",
    "\n",
    "    # 如果是文件（而不是文件夹），则读取内容\n",
    "    if not file_info.is_dir() and file_name.endswith('.npy') and 'press_0' not in file_name:\n",
    "      # 读取子文件内容\n",
    "      with zip_file.open(file_name, 'r') as sub_file:\n",
    "        data = np.load(sub_file)\n",
    "        pred_02[file_name]=data\n",
    "        print(file_name)"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "diff=[]\n",
    "test_preds={}\n",
    "for key,value2 in pred_02.items():\n",
    "  value1=pred_01[key]\n",
    "  diff.append(value1-value2)\n",
    "  test_preds[key]=0.3*value1+0.7*value2"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "!mkdir Example_C"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "for key,value in test_preds.items():\n",
    "  np.save(f'Example_C/'+os.path.basename(key),value)"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "!mkdir Dataset/data_test_A/\n",
    "!mv Dataset/Testset_track_A/Inference/* Dataset/data_test_A/\n",
    "!mv watertight_meshes.txt Dataset/data_test_A/"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "!python track_A.py"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "import zipfile\n",
    "import time\n",
    "\n",
    "folder_path =  f'Example_C/'\n",
    "with zipfile.ZipFile(f'B_result.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:\n",
    "  for root, _, files in os.walk(folder_path):\n",
    "    for file in files:\n",
    "      file_path = os.path.join(root, file)\n",
    "      zipf.write(file_path, 'Example_C' + os.path.basename(file_path))"
   ]
  }
 ]
}
